Biology subjects

Nash, M.

Publications and source records attributed to Nash, M..

16 recordsLinked to original sources

bioRxiv2026

GPNMB enables lysosomal adaptation required for myelin clearance and nerve regeneration after peripheral nerve injury

Recovery from peripheral nerve injury involves coordinated axonal and immune repair programs that promote tissue regeneration. However, the mechanisms regulating immune-mediated repair remain incompletely understood. Here, we demonstrate that glycoprotein non-metastatic melanoma protein B (GPNMB) is a key regulator of repair processes following peripheral nerve injury. In a mouse sciatic nerve crush model, loss of Gpnmb impaired functional recovery after injury. MRI and histological analyses revealed persistent myelin debris accumulation, defective debris clearance, and impaired macrophage lysosomal function within injured nerves. Mechanistic studies showed that GPNMB-deficient human iPSC-derived macrophages exhibited impaired lysosomal adaptation, reduced lysosomal fitness, and defective processing of myelin-associated cargo, resulting in inefficient degradation of injury-associated material. These defects were accompanied by impaired acquisition of repair-associated cellular programs. Together, our findings identify GPNMB as a critical regulator of lysosome-dependent repair programs during nerve regeneration and demonstrate that efficient GPNMB-dependent myelin processing is required for functional recovery following peripheral nerve injury.

neuroscience↗

bioRxiv2026

The Candida auris Adhesin Scf1 Mediates Broad Host-Protein Recognition through Shared Adhesive Regions

Candida auris (C. auris) is a rapidly emerging, multidrug-resistant fungal pathogen whose persistence is partly driven by adhesion to host tissues and abiotic surfaces. Surface-colonization factor 1 (Scf1) is a C. auris-specific adhesin implicated in surface adhesion, biofilm formation, virulence, and persistence in health care settings. However, the human proteins recognized by Scf1 and the response of these interactions to hydrodynamic flow and shear stress were previously unknown. Using yeast surface display, spinning-disk adhesion and FACS-based binding assays, we show that the N-terminal adhesive domain of Scf1 binds the cell-surface glycoprotein mucin-1 (MUC1) and the extracellular matrix proteins vitronectin (VIT) and fibronectin (FN), while exhibiting no detectable binding to collagen I, collagen IV, or fibrinogen. Hydrodynamic shear stress was found to tune ligand preference, with VIT being preferred at equilibrium and MUC1 being the more strongly bound target under flow. Epitope mapping by deep mutational scanning (DMS) of Scf1 against MUC1, VIT and FN identified shared binding patches consisting of predominantly cationic and aromatic residues involved in recognizing all three ligands. Mutations within these regions reduced binding without compromising yeast display expression levels, indicating that these residues are involved in ligand recognition and that mutations at these positions do not compromise global protein folding stability. These findings establish the Scf1 N-terminal domain as a semi-promiscuous ligand-recognition domain for a structurally diverse set of human proteins and suggest that a mechanosensitive adhesion mechanism contributes to C. auris colonization.

biochemistry↗

bioRxiv2025

Neural tracking of melodic prediction is pre-attentive

Musics ability to modulate arousal and manipulate emotions relies upon formation and violation of predictions. Music is often used to modulate arousal and mood while individuals focus on other tasks, suggesting that sophisticated musical prediction may be implicit and obligatory, operating regardless of attention. Here we investigated the role of attention in musical prediction by presenting participants simultaneously with a musical passage in one ear and an audiobook in the other. Participants were asked to attend to either speech or music to perform a recognition memory task. Musical prediction was investigated using a computational model of music which was trained on a corpus of Western music (long-term learning) and/or incrementally on each stimulus (short-term learning). While neural tracking of the acoustics of music, including encoding of note onset and pitch interval size, was enhanced by attention, musical prediction tracked the neural signal robustly regardless of attention. However, although neural tracking of long-term musical prediction based on previously-learned statistical patterns was unaffected by attention, tracking of short-term musical prediction learned from the structure of each stimulus was present only when the music was attended. Thus, attention is crucial for short-term musical learning and prediction but is not required for musical prediction based on long-term stylistic exposure.

neuroscience↗

bioRxiv2025

Molecular characterization of an adhesion GPCR signal transduction

Key cellular processes rely on the transduction of extracellular mechanical signals by specialized membrane receptors, including adhesion G-protein-coupled receptors (aGPCRs). While recent studies support aGPCR activation via shedding of the extracellular GAIN domain, shedding-independent signaling mechanisms have also been observed. However, the molecular basis underlying these distinct activation modes remains poorly understood. Here, we integrate single-molecule force spectroscopy, molecular dynamics simulations, and cell-based assays to elucidate the structural and dynamic mechanisms of ADGRG1 mechanotransduction. We show that shear stress induces distinct deformation pathways in the isolated GAIN domain, promoting tethered agonist (TA) exposure through loop rearrangements prior to domain shedding. In the full-length receptor, defined GAIN orientations and specific loop contacts with the 7-transmembrane (7TM) core enable allosteric TA engagement and signaling in the absence of GAIN dissociation. The directionality of the applied force dictates the activation pathway, favoring either GAIN shedding or intact GAIN-7TM coupling. These mechanisms align with both the basal activity and collagen-enhanced signaling of ADGRG1. Using deep learning-guided design, we engineered GAIN variants with tailored mechanical sensitivity, validating our model through predictable shifts in constitutive and ligand-induced signaling. Together, our findings establish a unified framework for aGPCR activation governed by GAIN dynamics and orientation, bridging mechanical and allosteric models of receptor function and providing new strategies for engineering mechanosensitive receptors and precision therapeutics.

biophysics↗

bioRxiv2025

Decoding Substrate Specificity in a Promiscuous Biocatalyst by Enzyme Proximity Sequencing

Substrate specificity is a defining feature of enzyme function, but its molecular underpinnings remain difficult to decode and engineer. Here, we leveraged enzyme proximity sequencing (EP-Seq) to systematically map how single-point and combinatorial mutations reshape the substrate preferences of D-amino acid oxidase (DAOx) from Rhodotorula gracilis, a model promiscuous enzyme. We generated [~]40,000 sequence-phenotype pairs, enabling us to profile the activities of [~]6,500 unique DAOx variants against five D-amino acid substrates with distinct physicochemical properties. Our analysis revealed that substrate-specific mutations are distributed throughout the enzyme structure. Mutations near the active site drive strong specificity shifts but also incur catalytic penalties, while distal mutations subtly rewire intramolecular contacts in order to modulate specificity with minimal loss of activity. We identified and validated positional hotspots that act allosterically to influence specificity, and characterized key variants that acquired exclusive substrate specificity or exhibited up to 230-fold changes in substrate preference. Combining mutations with complementary effects further sharpened substrate discrimination, enabling rational design of highly selective biocatalysts. This work provides a powerful framework for decoding enzyme specificity and provides unique foundational datasets to advance AI-guided enzyme engineering.

bioengineering↗

bioRxiv2024

Force-dependent Reorganization and Mechanostability of the Izumo1:Juno Complex Involved in Human Fertilization

Izumo1:Juno-mediated adhesion between sperm and egg cells is essential for mammalian sexual reproduction. However, conventional biophysical and structural approaches have provided only limited functional insights. Using atomic force microscopy-based single- molecule force spectroscopy and all-atom steered molecular dynamic simulations, we explored the role of mechanical forces in regulating the human Izumo1:Juno complex. Our findings reveal a multi-state catch bond capable of withstanding forces up to 600 pN- mechanostability rarely observed among eukaryotic protein complexes. We further found that this mechanostability was impaired in the infertility-associated mutant, JunoH177Q. Detailed steered molecular dynamics simulations show how force-dependent reorganization of the Izumo1:Juno complex engages new binding conformations to achieve this state of high mechanostability. Overall, this study significantly enhances our understanding of the mechanical underpinnings that regulate human fertilization.

biophysics↗

bioRxiv2024

Impact of Interval Censoring on Data Accuracy and Machine Learning Performance in Biological High-Throughput Screening

High-throughput screening (HTS) combined with deep mutational scanning (DMS) and next-generation DNA sequencing (NGS) have great potential to accelerate discovery and optimization of biological therapeutics. Typical workflows involve generation of a mutagenized variant library, screening/selection of variants based on phenotypic fitness, and comprehensive analysis of binned variant populations by NGS. However, in such cases, the HTS data are subject to interval censoring, where each fitness value is calculated based on the assignment of variants to bins. Such censoring leads to increased uncertainty, which can impact data accuracy and, consequently, the performance of machine learning (ML) algorithms tasked with predicting sequence-fitness pairings. Here, we investigated the impact of interval censoring on data quality and ML performance in biological HTS experiments. We theoretically analyzed the impact of data censoring and propose a dimensionless number, the Ratio of Discretization (RD), to assist in optimizing HTS parameters such as the bin width and the sampling size. This approach can be used to minimize errors in fitness prediction by ML and to improve the reliability of these methods. These findings are not limited to biological HTS techniques and can be applied to other systems where interval censoring is an advantageous measurement strategy.

bioinformatics↗

bioRxiv2024

Amber Codon Mutational Scanning and Bioorthogonal PEGylation for Mapping Antibody Binding Sites on Human Arginase-1

Epitope mapping is crucial for understanding immunological responses to protein therapeutics. Here, we combined genetic code expansion and bacterial surface display to incorporate S-allylcysteine (SAC) into human arginase-1 (hArg1) via Methanococcoides burtonii pyrrolysyl-tRNA synthetase. Using an amber codon deep mutational scanning and sequencing workflow, we mapped SAC incorporation efficiency across the hArg1 sequence, providing insights into structural and sequence dependencies of non-canonical amino acid incorporation. We used mutually bioorthogonal allyl/tetrazine and azide/DBCO chemistries to achieve site-specific PEGylation and fluorescent labeling of hArg1, revealing insights into SAC side chain reactivity and solvent accessibility of residues in hArg1. This system was further applied to determine the binding epitope of a monoclonal antibody on the surface of hArg1, providing high-resolution data on the impact of PEGylation residue position on antibody binding. Our method produces high dimensional data of non-canonical amino acid incorporation efficiency, site-specific functionalization enabled by mutually bioorthogonal chemistries, and epitope mapping of therapeutic proteins.

bioengineering↗

bioRxiv2024

Anchor Point Engineering on Anticalin Scaffolds for Enhanced Particle Adhesion to CTLA-4 Under Shear Stress

Achieving high binding strength and efficient delivery of molecular cargo to cells expressing biomarker targets is a significant challenge in drug delivery. Here, we investigated how altering the surface immobilization residue (i.e. anchor point) within a non-antibody binding scaffold called Anticalin can enhance particle adhesion to the immune checkpoint protein CTLA-4 on mammalian cells under shear stress. By introducing bio-orthogonal clickable amino acids into Anticalin at various positions and applying tension to the protein complex using single-molecule AFM force spectroscopy and bead-based adhesion assays, we elucidate the relationship between anchor point position and mechanostability of the Anticalin:(CTLA-4) complex. Multi-regression analysis of the physicochemical properties of the anchor points revealed that the distance from the anchor point on Anticalin to CTLA-4s center of mass was a major determinant of binding strength under shear flow. These results demonstrate how anchor point engineering can enhance particle adhesion and cellular delivery to CTLA-4 targets and provides a heuristic for choosing surface immobilization points of targeting proteins such that they withstand high mechanical forces.

biophysics↗

bioRxiv2024

Engineering the Mechanical Stability of a Therapeutic Affibody/PD-L1 Complex by Anchor Point Selection

Protein-protein complexes can vary in mechanical stability depending on the direction from which force is applied. Here we investigated the anisotropic mechanical stability of a molecular complex between a therapeutic non-immunoglobulin scaffold called Affibody and the extracellular domain of the immune checkpoint protein PD-L1. We used a combination of single-molecule AFM force spectroscopy (AFM-SMFS) with bioorthogonal clickable peptide handles, shear stress bead adhesion assays, molecular modeling, and steered molecular dynamics (SMD) simulations to understand the pulling point dependency of mechanostability of the Affibody:(PD-L1) complex. We observed diverse mechanical responses depending on the anchor point. For example, pulling from residue #22 on Affibody generated an intermediate unfolding event attributed to partial unfolding of PD-L1, while pulling from Affibodys N-terminus generated force-activated catch bond behavior. We found that pulling from residue #22 or #47 on Affibody generated the highest rupture forces, with the complex breaking at up to [~] 190 pN under loading rates of [~]104-105 pN/sec, representing a [~]4-fold increase in mechanostability as compared with low force N-terminal pulling. SMD simulations provided consistent tendencies in rupture forces, and through visualization of force propagation networks provided mechanistic insights. These results demonstrate how mechanostability of therapeutic protein-protein interfaces can be controlled by informed selection of anchor points within molecules, with implications for optimal bioconjugation strategies in drug delivery vehicles.

biophysics↗

bioRxiv2024

Decoding Stability and Epistasis in Human Myoglobin by Deep Mutational Scanning and Codon-level Machine Learning

Engineering protein stability is a critical challenge in biotechnology. Here, we used massively parallel deep mutational scanning (DMS) to comprehensively explore the mutational stability landscape of human myoglobin (hMb) and identify key mutations that enhance stability. Our DMS approach involved screening over 10,000 hMb variants by yeast surface display, single-cell sorting and high-throughput DNA sequencing. We show how surface display levels serve as a proxy for thermostability of soluble hMb variants, and report strong correlations between DMS-derived display levels and top-performing machine learning stability prediction algorithms. This approach led to the discovery of a variant with a de novo disulfide bond between residues R32C and C111, which increased thermostability by >12 {degrees}C compared to wild-type hMb. By combining single stabilizing mutations with R32C, we engineered combinatorial variants that exhibited predominantly additive effects on stability with minimal epistasis. The most stable combinatorial variant exhibited a denaturation temperature exceeding 89 {degrees}C, representing a >17 {degrees}C improvement over wild-type hMb. Our findings demonstrate the capabilities in DMS-assisted combinatorial protein engineering to guide the discovery of thermostable variants, and highlight the potential of massively parallel mutational analysis for the development of proteins for industrial and biomedical applications.

bioengineering↗

bioRxiv2023

Engineering an artificial catch bond using mechanical anisotropy

Catch bonds are a rare class of protein-protein interactions where the bond lifetime increases under an external pulling force. Here, we report how modification of anchor geometry generates catch bonding behavior for the mechanostable Dockerin G:Cohesin E (DocG:CohE) adhesion complex found on human gut bacteria. Using AFM single-molecule force spectroscopy in combination with bioorthogonal click chemistry, we mechanically dissociated the complex using five precisely controlled anchor geometries. When tension was applied through residue #13 on CohE and the N-terminus of DocG, the complex behaved as a two-state catch bond, while in all other tested pulling geometries, including the native configuration, it behaved as a slip bond. We used a kinetic Monte Carlo model with experimentally derived parameters to simulate rupture force and lifetime distributions, achieving strong agreement with experiments. Single-molecule FRET measurements further demonstrated that the complex does not exhibit dual binding mode behavior at equilibrium but unbinds along multiple pathways under force. Together, these results show how mechanical anisotropy and anchor point selection can be used to engineer artificial catch bonds.

biophysics↗

bioRxiv2023

Iterative Machine Learning for Classification and Discovery of Single-molecule Unfolding Trajectories from Force Spectroscopy Data

We report the application of machine learning techniques to accelerate classification and analysis of protein unfolding trajectories from force spectroscopy data. Using kernel methods, logistic regression and triplet loss, we developed a workflow called Forced Unfolding and Supervised Iterative Online (FUSION) where a user classifies a small number of repeatable unfolding patterns encoded as image data, and a machine is tasked with identifying similar images to classify the remaining data. We tested the workflow using two case studies on a multi-domain XMod-Dockerin/Cohesin complex, validating the approach first using synthetic data generated with a Monte Carlo algorithm, and then deploying the method on experimental atomic force spectroscopy data. FUSION efficiently separated traces that passed quality filters from unusable ones, classified curves with high accuracy, and identified unfolding pathways undetected by the user. This study demonstrates the potential of machine learning to accelerate data analysis, and generate new insights in protein biophysics.

biophysics↗

bioRxiv2023

Uncovering and engineering the mechanical properties of the adhesion GPCR ADGRG1 GAIN domain

Key cellular functions depend on the transduction of extracellular mechanical signals by specialized membrane receptors including adhesion G-protein coupled receptors (aGPCRs). While recently solved structures support aGPCR activation through shedding of the extracellular GAIN domain, the molecular mechanisms underpinning receptor mechanosensing remain poorly understood. When probed using single-molecule atomic force spectroscopy and molecular simulations, ADGRG1 GAIN dissociated from its tethered agonist at forces significantly higher than other reported signaling mechanoreceptors. Strong mechanical resistance was achieved through specific structural deformations and force propagation pathways under mechanical load. ADGRG1 GAIN variants computationally designed to lock the alpha and beta subdomains and rewire mechanically-induced structural deformations were found to modulate the GPS-Stachel rupture forces. Our study provides unprecedented insights into the molecular underpinnings of GAIN mechanical stability and paves the way for engineering mechanosensors, better understanding aGPCR function, and informing drug-discovery efforts targeting this important receptor class.

biophysics↗

bioRxiv2023

Direct Comparison of Lysine vs. Site-specific Protein Surface Immobilization in Single-molecule Mechanical Assays

Single-molecule force spectroscopy (SMFS) is a powerful method for studying folding states and mechanical properties of proteins, however, it requires surface immobilization of proteins onto force-transducing probes such as cantilevers or microscale beads. A common immobilization method relies on coupling surface-exposed lysine residues to carboxylated surfaces using 1-ethyl-3-(3-dimethyl-aminopropyl) carbodiimide and N-hydroxysuccinimide (EDC/NHS). Because proteins typically contain many lysine groups, this strategy results in a heterogeneous distribution of tether positions in the molecule. Genetically encoded peptide tags (e.g., ybbR) provide alternative chemistries for achieving site-specific immobilization, but thus far a direct comparison of site-specific vs. lysine-based immobilization strategies to assess effects on the observed mechanical properties was lacking. Here, we directly compared lysine- vs. ybbR-based protein immobilization in SMFS assays using several model polyprotein systems. Our results show that lysine-based immobilization results in significant signal deterioration for monomeric streptavidin-biotin interactions, and loss of the ability to correctly classify unfolding pathways in a multipathway Cohesin-Dockerin system. We developed a mixed immobilization approach where a site-specifically tethered ligand was used to probe surface-bound proteins immobilized through lysine groups, and found partial recovery of specific signals. The mixed immobilization approach represents a viable alternative for mechanical assays on in vivo-derived samples or other proteins of interest where genetically encoded tags are not feasible.

biophysics↗

bioRxiv2023

Dissecting the Biophysical Origins of Activity-Stability Tradeoffs in D-amino Acid Oxidase with Enzyme Proximity-Seq

Understanding the complex relationships between enzyme sequence, folding stability and catalytic activity is crucial for applications in industry and biomedicine. However, current enzyme assay technologies are limited by an inability to simultaneously resolve both stability and activity phenotypes and to couple these to gene sequences at large scale. Here we developed Enzyme Proximity Sequencing (EP-Seq), a deep mutational scanning method that leverages peroxidase-mediated radical labeling with single cell fidelity to dissect the effects of thousands of mutations on stability and catalytic activity of oxidoreductase enzymes in a single experiment. We used EP-Seq to analyze how 6,399 missense mutations influence folding stability and catalytic activity in a D-amino acid oxidase (DAOx) from R.gracilis. The resulting datasets demonstrate activity-based constraints that limit folding stability during natural evolution, and identify hotspots distant from the active site as candidates for mutations that improve catalytic activity without sacrificing stability. EP-Seq can be extended to other enzyme classes and provides valuable insights into biophysical principles governing enzyme structure and function.

bioengineering↗